Human activities perturb the large-scale water cycle by withdrawing large amounts of freshwater for agriculture, manufacturing, energy production and drinking water supply, and by operating dams/reservoirs. The risk that human water demand exceeds freshwater availability widely threatens human water security and ecosystem health, in particular in the face of climate change. Therefore, national-scale hydrological models need to integrate representations of human activities to anticipate and address water scarcity and to support the design of adaptation strategies beyond the local scale. However, the lack of detailed observational datasets of human influence at a national scale hinders the development and evaluation of integrated modelling approaches. This study focuses on processing a national observational dataset of human influence for hydrological modelling at the catchment scale in France, where climate change is expected to reduce water resources and increase water demand notably in the sector of irrigation. We collect data of water withdrawal, water release, reservoir operations from a large range of sources. These include national-scale datasets that are typically available at a coarse (annual) temporal resolution only and that are known to have large uncertainties, such as the French national database of quantitative water withdrawals. Covering a large spatial domain and attempting to account for uncertainties, our resulting dataset is a first step toward the development of robust integrated human-water system models at a national scale.